Transfer learning tries to leverage source domain knowledge to help learn models in the target domain, which mitigates the problem that the target domain has no label or few labels. [1]}, [2]} concluded the traditional transfer learning methods, and [3]}, [4]} introduced the deep transfer learning methods. Domain adaptation, as a branch of the transfer learning, solves the problem that a distribution shift exists between different domains and has been successfully applied in many applications, such as visual image classification [5]}, natural language processing [6]}, sentiment classification [7]}, etc. We roughly divide domain adaptation into two categories: 1) rough matching which includes domain-level matching, class-level matching, and both domain- and class-level matching; 2) sample-level matching which is a meticulous matching way.
